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comparison pylearn/algorithms/tests/test_mcRBM.py @ 1507:2a6a6f16416c

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fix import.
author Frederic Bastien <nouiz@nouiz.org>
date Mon, 12 Sep 2011 11:45:41 -0400
parents 7c51c0355d86
children b28e8730c948
comparison
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1506:2f69c9932d9a 1507:2a6a6f16416c
1 import sys 1 import sys
2 from pylearn.algorithms.mcRBM import * 2
3 import pylearn.datasets.cifar10 3 import numpy
4 import pylearn.dataset_ops.tinyimages 4 import theano
5 5 from theano import tensor
6
7 from pylearn.algorithms.mcRBM import mcRBM, mcRBMTrainer, mcRBM_withP, l2
8 #import pylearn.datasets.cifar10
6 import pylearn.dataset_ops.cifar10 9 import pylearn.dataset_ops.cifar10
7 from theano import tensor
8 from pylearn.shared.layers.logreg import LogisticRegression 10 from pylearn.shared.layers.logreg import LogisticRegression
11 from pylearn.io import image_tiling
12 import pylearn.dataset_ops.image_patches
9 13
10 14
11 def _default_rbm_alloc(n_I, n_K=256, n_J=100): 15 def _default_rbm_alloc(n_I, n_K=256, n_J=100):
12 return mcRBM.alloc(n_I, n_K, n_J) 16 return mcRBM.alloc(n_I, n_K, n_J)
13 17
67 _img = image_tiling.tile_raster_images(X, 71 _img = image_tiling.tile_raster_images(X,
68 img_shape=(R,C), 72 img_shape=(R,C),
69 min_dynamic_range=1e-2) 73 min_dynamic_range=1e-2)
70 image_tiling.save_tiled_raster_images(_img, fname) 74 image_tiling.save_tiled_raster_images(_img, fname)
71 75
72 batch_idx = TT.iscalar() 76 batch_idx = tensor.iscalar()
73 batch_range =batch_idx * batchsize + np.arange(batchsize) 77 batch_range =batch_idx * batchsize + numpy.arange(batchsize)
74 78
75 if dataset == 'MAR': 79 if dataset == 'MAR':
76 train_batch = pylearn.dataset_ops.image_patches.ranzato_hinton_2010_op(batch_range) 80 train_batch = pylearn.dataset_ops.image_patches.ranzato_hinton_2010_op(batch_range)
77 elif dataset == 'cifar10patches8x8': 81 elif dataset == 'cifar10patches8x8':
78 train_batch = pylearn.dataset_ops.cifar10.cifar10_patches( 82 train_batch = pylearn.dataset_ops.cifar10.cifar10_patches(
80 pca_components=n_vis) 84 pca_components=n_vis)
81 elif dataset == 'tinyimages_patches': 85 elif dataset == 'tinyimages_patches':
82 train_batch = pylearn.dataset_ops.tinyimages.tinydataset_op(batch_range) 86 train_batch = pylearn.dataset_ops.tinyimages.tinydataset_op(batch_range)
83 else: 87 else:
84 train_batch = pylearn.dataset_ops.image_patches.image_patches( 88 train_batch = pylearn.dataset_ops.image_patches.image_patches(
85 s_idx = (batch_idx * batchsize + np.arange(batchsize)), 89 s_idx = (batch_idx * batchsize + numpy.arange(batchsize)),
86 dims = (n_patches,R,C), 90 dims = (n_patches,R,C),
87 center=True, 91 center=True,
88 unitvar=True, 92 unitvar=True,
89 dtype=floatX, 93 dtype=theano.config.floatX,
90 rasterized=True) 94 rasterized=True)
91 95
92 if not as_unittest: 96 if not as_unittest:
93 imgs_fn = function([batch_idx], outputs=train_batch) 97 imgs_fn = theano.function([batch_idx], outputs=train_batch)
94 98
95 trainer = trainer_alloc( 99 trainer = trainer_alloc(
96 rbm_alloc(n_I=n_vis), 100 rbm_alloc(n_I=n_vis),
97 train_batch, 101 train_batch,
98 batchsize, 102 batchsize,
102 persistent_chains=persistent_chains) 106 persistent_chains=persistent_chains)
103 rbm=trainer.rbm 107 rbm=trainer.rbm
104 108
105 if persistent_chains: 109 if persistent_chains:
106 grads = trainer.contrastive_grads() 110 grads = trainer.contrastive_grads()
107 learn_fn = function([batch_idx], 111 learn_fn = theano.function([batch_idx],
108 outputs=[grads[0].norm(2), grads[0].norm(2), grads[1].norm(2)], 112 outputs=[grads[0].norm(2), grads[0].norm(2), grads[1].norm(2)],
109 updates=trainer.cd_updates()) 113 updates=trainer.cd_updates())
110 else: 114 else:
111 learn_fn = function([batch_idx], outputs=[], updates=trainer.cd_updates()) 115 learn_fn = theano.function([batch_idx], outputs=[], updates=trainer.cd_updates())
112 116
113 if persistent_chains: 117 if persistent_chains:
114 smplr = trainer.sampler 118 smplr = trainer.sampler
115 else: 119 else:
116 smplr = trainer._last_cd1_sampler 120 smplr = trainer._last_cd1_sampler
252 l1_penalty=1e-3, 256 l1_penalty=1e-3,
253 l1_penalty_start=sys.maxint, 257 l1_penalty_start=sys.maxint,
254 p_training_start=2*epoch_size//batchsize, 258 p_training_start=2*epoch_size//batchsize,
255 persistent_chains=False) 259 persistent_chains=False)
256 rbm=trainer.rbm 260 rbm=trainer.rbm
257 learn_fn = function([train_batch], outputs=[], updates=trainer.cd_updates()) 261 learn_fn = theano.function([train_batch], outputs=[], updates=trainer.cd_updates())
258 smplr = trainer._last_cd1_sampler 262 smplr = trainer._last_cd1_sampler
259 263
260 ii = 0 264 ii = 0
261 for i_epoch in range(6): 265 for i_epoch in range(6):
262 for i_batch in xrange(epoch_size // batchsize): 266 for i_batch in xrange(epoch_size // batchsize):
321 WINDOW_SIZE=8 325 WINDOW_SIZE=8
322 WINDOW_STRIDE=4 326 WINDOW_STRIDE=4
323 327
324 # put these into shared vars because support for big matrix constants is bad, 328 # put these into shared vars because support for big matrix constants is bad,
325 # (comparing them is slow) 329 # (comparing them is slow)
326 pca_eigvecs = shared(pca_dct['eig_vecs'].astype('float32')) 330 pca_eigvecs = theano.shared(pca_dct['eig_vecs'].astype('float32'))
327 pca_eigvals = shared(pca_dct['eig_vals'].astype('float32')) 331 pca_eigvals = theano.shared(pca_dct['eig_vals'].astype('float32'))
328 pca_mean = shared(pca_dct['mean'].astype('float32')) 332 pca_mean = theano.shared(pca_dct['mean'].astype('float32'))
329 333
330 def theano_pca_whiten(X): 334 def theano_pca_whiten(X):
331 #copying preprepcessing.pca.pca_whiten 335 #copying preprepcessing.pca.pca_whiten
332 return tensor.true_div( 336 return tensor.true_div(
333 tensor.dot(X-pca_mean, pca_eigvecs), 337 tensor.dot(X-pca_mean, pca_eigvecs),
352 h_list.append(h) 356 h_list.append(h)
353 g_list.append(g) 357 g_list.append(g)
354 358
355 hg = tensor.concatenate(h_list + g_list, axis=1) 359 hg = tensor.concatenate(h_list + g_list, axis=1)
356 360
357 feat_fn = function([feat_idx], hg) 361 feat_fn = theano.function([feat_idx], hg)
358 features = numpy.empty((60000, 11025), dtype='float32') 362 features = numpy.empty((60000, 11025), dtype='float32')
359 for i in xrange(60000//batchsize): 363 for i in xrange(60000//batchsize):
360 if i % 100 == 0: 364 if i % 100 == 0:
361 print("feature batch %i"%i) 365 print("feature batch %i"%i)
362 features[i*batchsize:(i+1)*batchsize] = feat_fn(i) 366 features[i*batchsize:(i+1)*batchsize] = feat_fn(i)
400 feature_logreg.w.value = numpy.random.RandomState(44).randn(11025,10)*.02 404 feature_logreg.w.value = numpy.random.RandomState(44).randn(11025,10)*.02
401 405
402 traincost = feature_logreg.nll(y_i).sum() 406 traincost = feature_logreg.nll(y_i).sum()
403 traincost = traincost + abs(feature_logreg.w).sum() * l1_regularization 407 traincost = traincost + abs(feature_logreg.w).sum() * l1_regularization
404 #traincost = traincost + (feature_logreg.w**2).sum() * l2_regularization 408 #traincost = traincost + (feature_logreg.w**2).sum() * l2_regularization
405 train_logreg_fn = function([x_i, y_i, lr], 409 train_logreg_fn = theano.function([x_i, y_i, lr],
406 [feature_logreg.nll(y_i).mean(), 410 [feature_logreg.nll(y_i).mean(),
407 feature_logreg.errors(y_i).mean()], 411 feature_logreg.errors(y_i).mean()],
408 updates=pylearn.gd.sgd.sgd_updates( 412 updates=pylearn.gd.sgd.sgd_updates(
409 params=feature_logreg.params, 413 params=feature_logreg.params,
410 grads=tensor.grad(traincost, feature_logreg.params), 414 grads=tensor.grad(traincost, feature_logreg.params),